Nonlinear dynamics of information overload: Impact on source localization in complex networks

Source localization in complex networks is a rapidly advancing field with numerous real-world applications, including determining the source of misinformation. In this work, we model information spread across several real-world and synthetic complex networks using our Generalized Fractional Susceptible-Infected-Recovered (GFSIR) model, which incorporates the information overload (IOL) phenomenon. Then, we use Pearson's correlation algorithm to identify information sources in these networks and investigate how information overload affects localization quality. Numerical simulations have shown that localization effectiveness decreases with the parameter $α$, which controls the strength of the IOL, and increases with the spreading rate $β$. Our comparison across various topologies reveals that localization is generally more effective in synthetic structures, with Erdős-Rényi networks exhibiting greater resilience to IOL than Barabási-Albert models. Furthermore, we identified a critical reversal in the impact of network density: while a higher average degree enhances localization when IOL is negligible, less dense networks perform better under strong overload. This phenomenon represents a significant departure from the behavior observed in standard epidemic models.